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| A High-Frequency Acquisition and Low-Latency Interaction Method for Distribution Network Data Based on Sensing-Communication Integrated |
| Wang Ruiqiuyu, Zhang Hongshuo, Zhang Sunxuan, Liao Haijun, Zhou Zhenyu |
| School of Electrical and Electronic Engineering North China Electric Power University Beijing 102206 China |
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Abstract High-frequency acquisition services in distribution networks have stringent requirements for real-time response capability. There is a contradiction of performance limitations under the current resource allocation method. Traditional resource allocation methods tend to optimize only one of the communication or sensing parties in isolation, making it difficult to make full use of resources and inefficient data transmission. This seriously affects the real-time monitoring and control operation of the power grid and fault response capability. Recently, some methods have been proposed to collaboratively optimize multidimensional resources, but most of them lack the consideration of association mechanism and collection frequency requirements. Aiming at these problems, this paper proposed a high-frequency acquisition and low-latency interaction method for distribution network data based on sensing-communication integrated, which can reduce the average weighted sum of end-to-end delay and operating costs of the sensing-communication integrated network.
First, the network architecture of high-frequency data collection and low-latency interactive for distribution networks based on sensing-communication integrated was designed. On this basis, models for acquisition devices scheduling, sensing data compression and transmission, edge data processing, as well as end-to-end delay and network operation cost were constructed. The cost of network operation was suppressed while reducing end-to- end latency by jointly optimizing sensing-communication variables such as device scheduling, data compression ratio, bandwidth allocation, and power control.
Subsequently, in order to maintain the operation level of the distribution service carried by each acquisition device, the device must complete a certain number of data acquisition, compression and transmission within a specified time slot. A long-term terminal scheduling frequency constraint was introduced to safeguard this requirement. Meanwhile, the long-term optimization problem was decoupled into a single time slot deterministic optimization problem by Lyapunov theory.
Then, for the mixed nonlinear integer programming problem containing multiple variables, a sensing- communication integrated intelligent resource allocation algorithm for high-frequency acquisition scheduling frequency sensing was proposed in two stages. In the first stage, an improved DQN algorithm was used to realize the device scheduling and data compression ratio selection strategy. A penalty term was introduced into the loss function calculation of traditional DQN to examine whether the long-term terminal scheduling frequency constraints are satisfied or not, which improves the algorithm’s ability to perceive the scheduling frequency of each device. In the second stage, the ADMM algorithm was used to optimize the transmission power and bandwidth of each device in a distributed way, which can greatly improve the convergence of the algorithm. The proposed algorithm ensures that the high-frequency acquisition and low-latency interaction requirements of distribution network services are well satisfied.
Finally, the effectiveness and reasonableness of the proposed algorithm was verified by simulation examples. Simulation results show that the proposed method can effectively reduce the average weighted sum of end-to-end delay and operating costs of the sensing-communication integrated network, which are reduced by 12.41% and 18.07%, compared with the comparison algorithm. Meanwhile, the proposed algorithm also fully considers the characteristics and demands of high-frequency acquisition services in the distribution network, which makes it much more aware of the device scheduling frequency and significantly enhances the learning speed. The authors’ team will further explore the deep integration of global perception of the power grid and integrated sensing and communication (ISAC).
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Received: 11 January 2025
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2026, Vol. 41 
